[MUSIC] [MUSIC] [MUSIC] [MUSIC] Hello again, and welcome to our second lesson on variables derived from digital elevation models in which we will introduce some important parameters for hydrological studies, notably how to calculate flow lines and the delimit catchment areas. In the last lesson, we introduced the most common variables that are are derived from digital elevation models. As Amadou Sall indicated, the goal of this final lesson on DEMs is to introduce another class of thematic variables. In many disciplines, elevation is an especially important variable that can be used to identify zones that - based on their derived topographic characteristics, like their slope, curvature or aspect – can either be favored or avoided. But digital elevation models possess other properties that can be exploited to create other useful variables. In this lesson we will show you how they are calculated and how they can be adapted for use in different applications, particularly in the context of natural hazard assessment. We will begin by introducing analyses that are based on lines of sight; these include viewshed analysis, drop shadows and projected shadows, and the calculation of solar irradiance. We will then move on to consider derived variables that are used in hydrology such as drainage lines, and we will also see how GIS can be used to delimit drainage basins. We will finish by showing you how some indicators can be used to assess natural dangers and what role they can play in the public health domain. [MUSIC] There are a few different approaches that can be used to generate lines of sight can from a digital elevation model. A line of sight is effectively a straight line that connects two points and is constituted by all of the pixels that are located along it. The first approach is called viewshed analysis, and is used to determine the entire area that is visible from a given vantage point, given here in red. This technique analyses all lines of sight with reference to the relief in question. All possible viewing directions are considered. In this analysis, a single vantage point is considered and all visible terrain, given in yellow is distinguished from the areas that are not – in white. This function is often used in impact assessments; for example, if we want to build a new construction, we can determine the entire area that will be visible from it once built. In the opposite sense, viewshed analysis could also be used to determine the visual impact that a wind turbine will have on the surrounding area That is: from which points will the turbine be visible? This type of analysis is also frequently used to evaluate the reception zones that would be created by installing a telephone antenna in a given location. In the figure in the lower left, the transmitter is placed at the center of the red circle, and the radius indicates the strength of the installation. Areas shown in brown are not within the transmitters field of view and would receive poor reception. Hillshade models can be created from digital terrain models and represent elevation data in a format that gives us the impression of relief. Hillshade models are created by generating lines of sight from a specific position in order to simulate a light source. Relief is simulated by modifying the shade of grey depending on the surface orientation with respect to the light source. Zones that are oriented in the opposite direction of the light source and those that exhibit an inclination superior to the light source are greyed out. This shadow is a drop shadow. If we instead simulate the true shadow that would be created by the relief it is called the projected shadow. Solar potential is equal to the irradiation received by a surface assuming a cloudless sky. It is modeled using a similar approach to that used for the creation of a hillshade model, but it considers all possible projected shadows. Each grid cell corresponds to one point on the DTM, and it the amount of solar irradiation that it receives is dependent on the height of the sun, the transparency of the atmosphere, the surface slope and aspect as well as the structure of the horizon line. The result is given in Kilowatt per hour per meter squared. The function must first define the 360-degree horizon for every point in the DTM, as in this illustration shown on the right of the screen. Next, specific algorithms, such as those in the freely-available SAGA program, then calculate the solar potential received by each pixel over a specified time period. For each geographic location, you must specify at which point, and at what time, the sun rises, and must also define its trajectory across the sky, and the time that it sets. The result of these algorithms is a uniformly-spaced grid, where each pixel value indicates the amount of potential solar energy that would be received at that precise location. Here, on the left we have a 25m resolution digital elevation model model that we used to calculate the potential solar irradiance that is accumulated over the course of one day. [MUSIC] Let’s move on now to look at DEM-derived thematic variables that are specific to hydrology. We will begin by introducing the concept of drainage lines. A drainage line is theoretical path that a drop of water would follow along the surface relief to reach the drainage basin. In order to make the necessary calculations, we assume that the surface is smooth and impermeable. To begin the slope must be calculated for each pixel in the DEM. Next, we go to each high point within the catchment area so those typically belong to the crest lines. and then the drainage lines are determined gradually by moving along the steepest surfaces. From the generated flow lines, we evaluate the drainage density. This parameter is commonly used in hydrology. It is equal to the total length of the fluvial networks per surface unit area. Drainage density is correlated with the average flow rate, mean annual precipitation or the rate of sediment production. There are multiple different approaches that can use DEMs in order to automatically delimit the surface area of a drainage basin. These approaches can be divided into two categories: the first of which identifies crestlines by beginning with fluvial outlets and gradually moving to points located at higher elevations. The second family of approaches also begin at the outlets, but function by aggregating the highest neighbouring points together. Gradually the lines converge towards the basin limits. The principle underlying this approach appears logical and simple, however results are often flawed if there are local minima, or errors can be created in the presence in the presence of saddle-shaped land forms or along ridge-lines. In 1991, Vincent and Soille proposed an alternative method to avoid these problems; we will describe the principle with a simple 1-dimensional DEM made up of 10 elevation points. DHere, the points or pixels are numbered from 1 to n depending on their initial order. We then create a figure with the point number given by one axis and the elevation given on the other. In parallel, we also create an elevation frequency diagram with the numbered points. In this example 2 pixels belong first elevation class, given in green, 5 are in the second class – these are the points in orange, and 3 points make up the class with the highest elevation – these are the blue points. The algorithm then identifies the lowest point or points. If they are isolated, they form the seed for one catchment. So, in our example, pixel 1 is the lowest value. It constitutes the first point of the catchment area given in blue. LThe next lowest point – number 6 – is also a minimum and it is selected next. Because it is not located adjacent point 1 it forms the starting point for another catchment – this time given in red. If no other points are found at the same elevation, we move up an elevation level until we encounter a new point. As point 2 is adjacent to a point that has already been allocated to the blue catchment area, point 2 is also assigned to this basin. We follow the same process until all of the pixels have been assigned to a basin. The system works as if the relief were progressively inundated by groundwater until the entire study area is submerged. When two basins join up their borders converge to form a ridge. [MUSIC] We will now demonstrate how these DEM-derived variables can be applied in a real case. All of the examples that we will present here have been taken from an open access scientific publication, in each of which, the applied methodologies are described in detail. The first example that we will review aims to identify floodable areas in Akure a city in Nigeria with approximately 495,000 inhabitants. The city has played a central role in the local political and economic climate, and correspondingly has been the subject of significant population growth. The rainy season in Akure lasts approximately 7 months, between April and October, and experiences on average, a yearly 1500mm of rainfall per square meter. Because of the growing demand for building land, the city is under intense pressure to identify potential flood zones in order to ensure that they are not considered for future development. In this study, Landsat satellite images were classified to characterize the land use changes over the course of the study period – from 2002 to 2011. From these classified images, the authors were able to confirm that, in the region, the built environment had undergone significant growth, while a decrease in riparian vegetation has led to a reduction in the soil’s absorption capacity. They also used a digital terrain model in their analysis; the DTM they used had a 90m spatial resolution and was sourced from NASA’s Shuttle Radar Topography Mission, or SRTM. Using the DTM, they calculated the slope of the study area, and then they were able to generate the associated drainage lines. Next, the authors integrated data on mean annual precipitation that was collected at multiple different locations throughout the study area. The different layers that were used in the analysis, like the land use map, the drainage lines and the precipitation data were weighted as a function of their probability of contributing to a flood. Then a multicriteria approach was used to combine the layers and create a flood risk map that evaluated the vulnerability of the study area according to three classes. The second example that we will consider here evaluated the risk of rock falls. The study area was situated in the Dades catchment located in Morocco and consisted of an unstable zone that was sensitive to landslides. The objective of the study was to create a map that illustrated the risk of rockfalls. In order to create the map, a number of different criteria were considered. First, a DTM with a spatial resolution of 90m was acquired from SRTM and used to evaluate the surface slope throughout the study area. From this, the authors confirmed that 30% of the study area exhibited slopes that were either steep or very steep. In addition, the zone located uphill was strongly sloped and hilly, which undoubtedly gives rise to the regions unstable character. The authors also digitized geological maps in order to account for the lithology. Indeed, the lithological composition and geologic structure play a strong role in determining landslide risk. Calcites and dolomites, represented in light blue on the map; marly limestones, given in dark blue; conglomerates and clays, in yellow, are all sensitive to movement. Based on the geological maps, the authors confirmed that 50% of the study area is constituted of these highly sensitive materials. The stability of rock formations is primarily controlled by the density of tectonic fractures and their orientation. As such, another map layer was created from field surveys and satellite imagery in an effort to localize these features. In this analysis, this is an especially important criterion because the High Atlas Mountains are in the process of undergoing tectonic uplift and these fractures constitute an important destabilizing factor. Information of the slope characteristics, the sensitivity to movement and the degree of fracturing were combined in order to create a single risk map. Each factor was divided into 5 qualitative classes that ranged from “very low” to “very high”. In the resultant risk map, each pixel is attributed by a value that is characterized by a combination of the qualitative classes belonging to each of the three criteria. The final map qualifies the hazard level according to 5 classes that range from “very low risk” to “very high risk”. The final result indicates that 25% of the catchment area is subject to a very high level of risk. [MUSIC] We will finish with an example taken from the public health domain to illustrate that DEMs provide invaluable information to a vast range of applications. This study on schistosomiasis was published in 2015 in PLOS Neglected Tropical Diseases. Schistosomiasis is the most widely-spread water-borne disease in all of Sub-Saharan Africa. It is transmitted through freshwater snails who act as intermediate hosts for the parasite. The goal of the study was to identify potential habitats for the snail vector in order to determine which zones were high risk areas for schistosomiasis transmission. The study was conducted in a region in Burkina Faso, near Ouagadougou. Multiple environmental variables were taken into account to delimit potential vector habitats. Among them were the ephemerality of stagnant water bodies, which was calculated from RapidEye images with a 6.5 satellite resolution and from 30m resolution Landsat images that were taken at different times. Water temperature also influences snail mortality; this was derived from the thermal band of the satellite images. Vegetative cover was also analysed as it can affect snail metabolism. Finally, using a 30m DEM sourced from Aster, the slope of the surface was derived in order to estimate water depth and flow velocity. An additive multicriteria function was then used to weight and combine the different variables and create an index that characterized an environment’s habitat potential. This index was used to estimate the risk to determine whether a specific zone was favorable to schistosomiasis transmission. The redder the pixel is, the higher the risk of transmission. A map such as this could prove very valuable for informing prevention and control measures in order to limit the diffusion of the disease. [MUSIC] In this lesson, we presented a number of DEM-derived thematic variables that can be calculated to supplement feasible analyses with the help of measurements of slope, curvature and orientation. Viewshed analyses in particular have found use in a range of ecology-related applications, for example if we want to assess the impact that a new construction will have on the surrounding landscape. Further, given that renewable energies are gaining increasingly more support, the possibility to calculate potential solar insolation also constitutes an important variable. DEMs also play an important role in hydrology, and now you will now how you can use the to calculate drainage lines and to delimit catchment areas. To finish, the examples that we present have also illustrated the potential that DEMs have for creating effective prevention measures. Floods, landslides, urban development or public health there are a multitude of domains that can benefit from information derived from DEMs. And that is it for DEMs, in the next lesson we will see how GIS can be used to integrate different data types. [MUSIC] [MUSIC]
